Exploration of Underwater Structures with
Cooperative Heterogeneous Robots
Dirk Spenneberg
Christoph Waldmann
Richard Babb
Robotics Group
MARUM
Underwater Systems Laboratory
Dept. of Math. and Computer Science
Center of Marine Environmental Sciences Southampton Oceanography Centre
University of Bremen
University of Bremen
Southampton SO14 3ZH
Bibliotheksstr. 1, 28359 Bremen, Germany Leobener Str., 28359 Bremen, Germany
UK
[email protected]
[email protected]
[email protected]
Abstract— This paper describes ideas in the field of cooperative
underwater robotics, which can considerably enhance the exploration capabilities of underwater robots. Three heterogeneous
planned or existing underwater robots are presented and possible
approaches for cooperative behavior are discussed. Possible
application scenarios for future deployment are presented.
I. I NTRODUCTION
This paper describes ideas in the field of cooperative
underwater robotics, which would considerably enhance the
exploration capabilities of underwater robots. The authors
believe that it will be fruitful to form a bridge between the
work on cognitive, cooperative, and adaptive robotics in the
artificial intelligence and autonomous robots community and
the work in marine underwater robotics, with the goal to
develop concepts to develop, introduce, and test state of the
art robotic approaches in an underwater environment. The use
of multiple underwater agents for defined tasks is currently
an important topic within civil and military robotic research
although all attempts are still in their infancy. Several projects
(like [6]) are underway that make use of homogeneous platforms to evaluate cooperative behavior. In this case cooperative
behavior is realized as swarming behavior. Other cooperative
concepts exist for individual AUV systems where the control
algorithm are based on behavior methods [7].
Cooperative behavior in underwater robots will enable new
observational strategies in the fields of geo-sciences, environmental monitoring, and marine biology. As a first result
of using cooperative heterogeneous systems in underwater
missions, we expect a significant enhancement in the quality
of underwater exploration in unknown and difficult sea-floor
terrain. Underwater exploration scenarios are an ideal proving
ground for the idea of cooperative heterogeneous robots, because the underwater environment is optimal for making use of
agents which differ largely in their morphology, their mobility,
and their sensors. Deployable robots can have a very different
way to act and to perceive their environment and thus they
will need / have different ways to represent their surroundings.
From the embodied cognition point of view, it is highly
interesting to develop mechanisms to exchange, to translate,
and to integrate these different spatial representations. In this
paper, we present first ideas of how we can use already existing
Fig. 1.
The Autosub
and planned underwater robots to cooperate in an underwater
exploration scenario.
II. T HE H ETEROGENEOUS ROBOTS
In difficult terrains like the bottom of a chasm or inside a
cave, communication means are very limited. In comparison
to a single system, a group of cooperating robots has higher
chances to deal with these limitations; thereby the operational
range for a scientific mission can be largely extended. Groups
of heterogeneous robots open up new ways to make use of
communication networks. For example, a free swimming robot
can serve as a relay station for bottom-bound robots. The
Southampton Oceanographic Centre has developed over the
last ten years the free swimming AUV Autosub (see fig. 1),
intended for both scientific and commercial applications [1].
The vehicle is a streamlined torpedo shape seven meters long
by one meter diameter, with a cruciform tail, paired elevators
and rudders, and an exposed propeller driven by a direct drive
low speed DC motor. There are no forward hydroplanes or
thrusters and the ballast is preset though there is an emergency
abort weight. The hydrodynamic skin is a free flooded fiberglass shell; within are a variety of pressure vessels, allowing
maximum flexibility in sensor payload configurations. Power
is normally supplied by a disposable battery of up to 5000
alkaline ’D’ cells, though rechargeable cells can be used for
shorter missions.
Communications and control are based on the LONworks real
time networking system which allows an essentially unlimited
number of nodes on a four wire bus. Processing power at
nodes is augmented by additional processors, e.g., PC or DSP
modules, as necessary. Navigation is based on a combination
of an optical Inertial Navigation System (INS), a bottom
referenced acoustic Doppler sensor, a short baseline acoustic
system on the mother ship, a GPS receiver for use on the
surface, and various other low performance sensors for use as
fallbacks. There is an acoustic communication system capable
of low data rates (sub kilobaud) at multi km ranges.
Like all current AUV’s, the Autosub design is dominated by
the need to conserve energy and to take advantage of scaling
laws favoring large vehicles. The inevitable result is a design
with limited thrust to weight ratio, poor maneuverability
and inability to hover. It is these limitations which make
cooperation with other robot types advantageous.
More details on Autosub can be found in [2]. The MARUM
has a bottom -bound four-wheeled robot C-MOVE [3] which
is still in development but already operable (see fig. 2). Most
underwater vehicles are operated in a free flying mode which
makes thruster propulsion necessary. However, for vehicles
that are supposed to investigate the ocean floor bottom traction
systems are more favorable. In the ocean mostly caterpillar
type propulsion are in use. This results from the fact that
corresponding vehicles are for special heavy duty tasks in
the offshore business like burying submarine cables, requiring
superior drawbar performance. For a completely autonomous
operation of an autonomous, underwater measuring vehicle,
this concept has to be reconsidered under the aspects of energy
efficiency, low impact on the area to be sampled, ease of
control and maintenance.
The C-MOVE vehicle is a vehicle demonstrator has been developed as part of a cooperation between the University of Bremen/MARUM and the German Aerospace Center (DLR). The
partners developed new concepts by combining the expertise in
deep sea engineering and planetary exploration technologies.
The C-MOVE is designed to operate autonomously down to
6000 m depth in the ocean. It has four wheels for propulsion
which are all individually driven by electric motors and can
be steered individually. It is meant for investigations in deep
abyssal plains to investigate biogeochemical parameters of the
water sediment interface.
The Robotics Group of the University of Bremen is in the
process to develop a new underwater walking robot for rough
and steep terrain, which will be based on the bio-mimetic
technology of the thoroughly tested walking outdoor-robot
SCORPION1 (see fig. 3). The SCORPION robot has eight legs
and measures 65cm from front to back. The width depends on
the posture of the legs and varies between 20cm and 60cm. In
a typical M-shape walking position the robot is 40cm wide.
The robot weights 11.5 kg including the 3.0 Ah batteries.
1 sponsored
by DARPA (Grant No. N0014-99-1-0483) and NASA
Fig. 2.
Fig. 3.
The C-MOVE Vehicle
The 8-legged SCORPION Robot
Each leg has 3 DOF: a thoracic joint for protraction and
retraction, a basal joint for elevation and depression, and a
distal joint for extension and flexion of the leg. Thus the
whole system comprises 24 joints. The joints are actuated by
24V, 6W DC-Motors which drive planetary gears. By using
its biomimetic control approach the SCORPION is able to
walk robustly over a high variety of different substrates like
rocks, sand, mud, grass, concrete, and asphalt. Its maximum
speed over flat terrain is half of a body length (30cm/sec).
It can climb up ramps up to 35% and still overcome small
obstacles, like 8cm high pipes. More details can be found in
[4], [5]. Possible future fields of application for underwater
robots based on legged locomotion are the work in dangerous,
highly unstructured, rough, and unpredictable environments,
where mobility and the ability to attach itself to structures is
critical, e.g., to withstand strong current.
We have chosen these platforms because free swimming
robots are well suited to map medium scale (10’s to 100’s
of meters) structures of the ocean floor, while bottom-bound
robots enable small scale measurements (below 1 meter)
and allow to explore the physical properties of the sediment
and biological colonization in detail. Furthermore, AUVs like
Autosub are not well suited to near bottom operations in
complex and steep topography. These robots have different
energy needs, different travel range, different communication means, and they comprise very different sensor-systems.
Whereas free-swimming AUV perceives the structures at the
sea floor remotely by sonar and video, the wheeled and the
legged robot can perceive the sea floor directly by interacting
with it. Furthermore, the wheeled system can be used to
explore large areas of flat ground and the legged system to
explore rough terrain. The representation of the environment
in the free-swimming AUV and the C-MOVE will include
mainly metric data, whereas the representation in the legged
robot will be more based on topological data, which will
be based on categories the system can learn on the basis of
its proprioceptive / tactile data. A complete representation of
the environment, which combines these three different sensorviews, would provide a more complete picture and can be
used to refine the exploration strategy of each vehicle, e.g., a
detailed mapping of a part of the sea-floor structures by the
bottom-bound agents would enable the free-swimming robot
to lower its security bounds and to move into closer distance
to the sea-floor.
III. T HE C OOPERATIVE A PPROACH
Recently, in the robotics community cooperative approaches
to navigation and exploration of unknown environments gained
more and more attention, because using groups of robots
increased the overall stability of the build maps and reduced
the problem of the necessary simultaneous self-localization
while mapping (SLAM). Thrun [8] developed an efficient
probabilistic algorithm to address this problem in which a
team of robots builds a map online, while simultaneously
accommodating errors in their odometry. At the core of
the algorithm is a technique that combines fast maximum
likelihood map growing with a Monte Carlo localizer that
uses particle representations. The combination of both yields
an online algorithm that can cope with large odometric errors
typically found when mapping environments with cycles. The
algorithm can be implemented on multiple robot platforms,
enabling a team of robots to cooperatively generate a single
map of their environment.
Reikleitis et. all present in [9] a pair of cooperating robots
to test multi-robot environment mapping algorithms based on
triangulation of free space. The robots observe one another
using a robot tracking sensor based on laser range sensing
(LIDAR). The environment mapping itself is accomplished
using sonar sensing. The results of this mapping are compared
to those obtained using scanning laser range sensing and the
scan matching algorithm. They show that with appropriate
outlier rejection policies, the sonar-based map obtained using
collaborative localization can produce better results than the
superior laser range sensing technology. In [10] Burgard et.
all consider the problem how the overall exploration time
can be efficiently reduced by using cooperative robots. The
key problem to be solved in the context of multiple robots is
to choose appropriate target points for the individual robots
so that they simultaneously explore different regions of the
environment. They present an approach for the coordination
of multiple robots which, in contrast to previous approaches,
simultaneously takes into account the cost of reaching a target
point and its utility. The utility of a target point is given by
the size of the unexplored area that a robot can cover with its
sensors upon reaching that location. Whenever a target point is
assigned to a specific robot, the utility of the unexplored area
visible from this target position is reduced for the other robots.
This way, a team of multiple robots assigns different target
points to the individual robots. This is a good example of a
simple coordination of multiple robots, which significantly
reduces the exploration time compared to previous approaches.
Robots capable of operation at multi km depths are
severely constrained by the pressure rating / payload / energy
capacity tradeoff. Free swimming AUV’s will probably be
unable to afford the weight or energy for six degree of
freedom thrusters so will be unable to interact mechanically
with the seabed , to hover or to approach close to the seabed
in rugged terrain, and their mission duration will be limited
to tens of hours by propulsion energy requirements. Bottom
robots will have to be relatively small, perhaps 1 - 2 meters
across and will have limited capability to move in rugged
terrain, to deploy sensors requiring a high viewpoint, to
communicate and perhaps to cope with soft sediments. We
believe that a cooperating community of different types of
robot can overcome many of these difficulties. For simplicity
and generality we consider a free swimming AUV a walking
robot and a crawler, though other combinations may be
advantageous. Possible modes of cooperation include:
1) A preliminary site survey by the AUV operating alone.
The AUV is well able to carry out such a survey in a
lawn mower pattern at a relatively high and therefore
safe altitude using sensors such as sidescan and bathymetric sonars, sub bottom profilers, and cameras.
2) A more detailed planning survey by the robots operating
together. Ambiguous but worrying features from (1)
would be investigated by directing the crawler to them.
3) An individual robot in difficulty could call on assistance.
For example a crawler encountering unexpectedly steep
slopes could request a high resolution bathymetric survey by the AUV aimed at finding a safe route around
or through the terrain. A survey by a bottom robot
might enable a large and not very manoeuvrable AUV
to operate closer to the bottom in safety.
4) Navigation in the deep ocean is bound to present difficulties. Use of a short baseline system from the mother
ship with errors of tens of meters is of limited value.
One solution might be a long baseline acoustic system
using bottom mounted transponders; systems of this kind
with cm accuracy have been developed for underwater
archaeology. Transponders might be placed in suitable
positions by the bottom robots who would then com-
mand the AUV to determine the relative positions of
the transponder network by acoustic ranging at a high
altitude ( tens to hundreds of metres ) where reliable
acoustic paths exist. Alternatively , an AUV fitted with
an Inertial Navigation System might command the bottom robots to remain stationary without knowing their
exact positions and then determine the velocity drift in
its INS by acoustic ranging on them.
5) Bottom robots operating in rugged terrain are likely
to have communication difficulties. The AUV could
alleviate this by acting as a relay station either between
the bottom agents or between them and the mother ship.
All these forms of cooperation require communication, but
the characteristics of the links are unclear, particularly the
required data rates and propagation times, whether communication must be strictly deterministic, the extent of data
compression and feature extraction necessary for voluminous
data such as sidescan images. Authority relationships are
also open to question. Should a single robot be in overall
charge, responsible for matters such as mission replanning
and adaptation to equipment faults ? If so, which ? These
are very important questions for which we hope to find
answers in future cooperation experiments. For setting up these
experiments, it is important, that suitable application scenarios
are found, which provide constraints for the vehicles and the
possible cooperative strategies.
IV. A PPLICATION S CENARIOS
The above described differences between the robots will
allow investigating the patchiness in an individual physical
parameters or the varying biological and chemical conditions
within a defined area of interest.
A. Hydrothermal Vent Fields
These conditions can be found for instance in regions where
hydrothermal vents are present. For a future exploration of
these regions, it would be of interest to investigate different
colonisations with organism depending on the environmental
conditions. Typically, in hydrothermal vent regions mussel
banks and colonies of tube worms are found in regions close
to the orifices of the hydrothermal vents. A specific task for
the robot team could be to investigate these regions with
hydrothermal vent activity. The free swimming AUV could
be used to make a general survey of the region and to identify
sources of hot water outflow. After that, the bottom bound
robots will be released to spots of interest and will start
evaluating the region. The collected information is collected in
a central controller and will be updated permanently during the
mission. With this information available for all three types of
robot, a more focused investigation of the area will be started
by redirecting the robots to points of interest or areas where
temporal intermittent events occur (geyser). The cooperative
approach therefore will allow for a flexible strategy to investigate a region of high variability. To investigate biological
phenomena, it is necessary to address the issue of the changing
morphology of the seafloor. Some organism can only survive in
a well protected environment which could mean rough terrain.
Others like bacteria or musseles are often found in large
communities covering more flat terrain. The differing scale
coverage of the robots therefore can be used to investigate
differing phenomena while integrating common parameters
like for instance currents into the overall picture.
B. Under Ice Exploration
Ice shelves are one of the most inaccessible and most poorly
known environments on earth. Knowledge of these regions is
fundamental to the understanding of issues such as the role
of the ocean in climate change, ice melting, and the biology
beneath the ice shelf. Ice shelves are the floating edges of ice
sheets that cover Greenland and Antarctica. These ice sheets
contain 77 per cent of the world’s freshwater. Ice shelves have
sea water cavities beneath them that open to the ocean at
their edges. These cavities may be several hundred kilometres
long, with water several hundred metres deep under ice up
to a kilometre thick. To be able to investigate these huge
structures, new concepts have to be developed that necessarily
have to utilise autonomous agents. A concerted deployment
of heterogeneous, autonomous, mobile robots is attractive to
address the above mentioned issues of scientific investigations
because they allow for a flexible and targeted observation
strategy over long time periods. Beside the topography of
the sea floor below the ice, the structure, thickness and the
detailed topography of the bottom side of the ice cover,
the colonisation with organisms and the feedback between
biological and physical processes are scientific objectives of
paramount interest. The investigation of long term changes
of this sensitive environment and its causes will result in
a better understanding of the impact of climate changes on
the entire system. Possible application scenarios could be the
deployment of the AUV AUTOSUB again to conduct site
surveys to find regions of interest and acting as a central relay
station for communication purposes. The other two vehicles,
C-MOVE and SCORPION, can be deployed directly under the
bottom side of the ice shelves to be able to access particular
regions that appeared important from the AUV survey.
V. C ONCLUSION
After outlining and discussing the difficulties and the
possibilities of future cooperative underwater robotics, recapitulating, we see a high potential for transferring ideas
on cooperative robots into the field of underwater robotics.
Especially, because of the high uncertainty of sensor measurements in underwater environments, techniques of cooperative
probabilistic mapping approaches are very likely to improve
the correctness of self localization algorithms significantly.
Furthermore, heterogeneous robots allow to access new areas
of high scientific or commercial interest. Thus we believe
that it is worthwhile to pursue these ideas and we plan to
implement these with the above described robotic systems.
As a first step, we will implement a simulation for further
evaluation of the foreseen application scenarios.
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